Difference between revisions of "User:Grberlstein"
From REU@MU
Grberlstein (Talk | contribs) (→Day 1 (6/5)) |
Grberlstein (Talk | contribs) (→Day 1 (6/5)) |
||
| Line 44: | Line 44: | ||
==='''Day 1 (6/5)'''=== | ==='''Day 1 (6/5)'''=== | ||
*Refined K-means implementation with the K-means++ seeding described in the [https://datasciencelab.wordpress.com/2014/01/15/improved-seeding-for-clustering-with-k-means/ Data Science Lab] article | *Refined K-means implementation with the K-means++ seeding described in the [https://datasciencelab.wordpress.com/2014/01/15/improved-seeding-for-clustering-with-k-means/ Data Science Lab] article | ||
| − | * | + | *Tested the algorithm on random Gaussian distributions, rather than random points |
*Experimented with visual plotting of the algorithm using Seaborn and Matplotlib | *Experimented with visual plotting of the algorithm using Seaborn and Matplotlib | ||
Revision as of 22:34, 6 June 2017
Contents
Griffin Berlstein
Nominally a person.
Readings
Background
Algorithmic Ethics
- Ethics of Algorithms
- Is There an Ethics of Algorithms?
- Toward an Ethics of Algorithms
- Quantifying Search Bias
- Understanding and Designing around Users' Interaction with Hidden Algorithms in Sociotechnical Systems
Clustering and Data Science
Project Log For Summer 2017
Week One (5/30 - 6/2)
Day 1 (5/30)
- Attended REU orientation
- Obtained ID card and computer access
- Met with Dr. Guha and discussed broad ideas surrounding the project
Day 2 (5/31)
- Attended Library orientation
- Finished reading Ethics of Algorithms by Thijs Slot. This was the last of the pre-REU reading.
- Started reviewing the basics of Python
- Given crime data sets to review by Dr. Guha
Day 3 (6/1)
- Attended a meeting on proper research practices by Dr. Factor
- Set up direct deposit
- Reviewed the basics of GitHub
- Continued to review Python
- Examined crime data and the various ways it was made publically available
Day 4 (6/2)
- Moved mentor meeting to Wednesday due to scheduling issue
- Started reading background information provided by Dr. Guha
- Set up Jupyter notebook and the various dependent libraries
- Created rough implementation of K-means clustering on random data
- Obtained card access to Dr. Guha's lab
- Posted rough, pre-discussion milestones
Week Two (6/5 - 6/9)
Day 1 (6/5)
- Refined K-means implementation with the K-means++ seeding described in the Data Science Lab article
- Tested the algorithm on random Gaussian distributions, rather than random points
- Experimented with visual plotting of the algorithm using Seaborn and Matplotlib
Day 2 (6/6)
- Attended RCR training
- Finished reading the relevant sections of Algorithms for Clustering Data
- Experimented with Scikit-learn's implementation of K-means
